Gastos Casa
09 June, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0008e+09 2 5.1412 0.006 **
## lag_depvar 2.6295e+11 1 2701.5295 <2e-16 ***
## Residuals 8.1467e+10 837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1816.297 16273.97 0.1461666
## 2-0 31369.188 23230.288 39508.09 0.0000000
## 2-1 24140.350 19429.962 28850.74 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 776 40161.71 2 36232.14
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## 782 44650.71 2 39438.43
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## 785 44134.14 2 38280.43
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## 789 45699.14 2 42564.29
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## 792 43772.86 2 50018.43
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## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
## 836 61333.14 2 57700.14
## 837 59230.71 2 61333.14
## 838 49195.00 2 59230.71
## 839 55436.43 2 49195.00
## 840 50353.14 2 55436.43
## 841 43194.86 2 50353.14
## 842 47539.71 2 43194.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 685 53603.45 22063.677
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2018.64229 4040.26745 -537.93319 2438.19179 -2969.35751 518.84572
## 8 9 10 11 12 13
## -5655.70179 -1187.86306 -3966.19550 -418.10382 -4939.82002 -1609.59751
## 14 15 16 17 18 19
## -899.92233 377.35432 -3242.90761 -377.96290 -2130.10496 6604.13346
## 20 21 22 23 24 25
## -1529.14548 -1208.29204 1475.58776 -1186.59553 234.63323 1695.01878
## 26 27 28 29 30 31
## -7101.95523 947.59638 8192.60592 418.77717 -13.32473 -2399.84894
## 32 33 34 35 36 37
## 1576.92746 4573.32799 1127.92021 2392.52636 -1866.81415 4609.09000
## 38 39 40 41 42 43
## 4304.44307 -2274.66615 -2980.54492 -1109.41047 -10740.81758 7289.65607
## 44 45 46 47 48 49
## 2557.67154 1367.26846 8105.57392 686.72007 6529.29802 6715.05088
## 50 51 52 53 54 55
## -5881.56618 -4794.86501 -5059.30159 -7928.27701 6129.91074 -4076.38619
## 56 57 58 59 60 61
## -4894.21861 3855.78690 887.88081 -32.06937 142.25413 -4996.41256
## 62 63 64 65 66 67
## 18126.68633 3640.97980 -3645.71115 5925.69957 7344.56137 14639.63340
## 68 69 70 71 72 73
## 1694.35451 -13211.50085 -1305.14074 4644.50710 -4899.21216 -4403.54122
## 74 75 76 77 78 79
## -10496.48120 2467.88937 -5398.60365 1064.97880 -6865.03309 548.19739
## 80 81 82 83 84 85
## -2353.30127 -2692.57976 -3930.56901 -536.17246 2315.93730 3763.87776
## 86 87 88 89 90 91
## 477.89484 -483.71751 197.31567 4302.52860 -1162.57560 1151.24439
## 92 93 94 95 96 97
## -2064.14106 -1043.96476 177.89982 275.06790 -7483.78433 2392.22923
## 98 99 100 101 102 103
## -8602.04741 -2939.74330 -4038.37144 -1735.33381 -1259.75666 3182.64406
## 104 105 106 107 108 109
## -2340.55157 2595.51549 -1157.11941 971.69060 2588.18718 -3153.48761
## 110 111 112 113 114 115
## -4722.10006 -849.16842 1904.60852 11694.50224 -1242.14062 2669.01000
## 116 117 118 119 120 121
## 4263.23899 3502.97582 -1099.69129 -4715.96139 -3723.49069 2320.55522
## 122 123 124 125 126 127
## -1731.92528 1341.23403 8859.01942 847.58901 130.95288 -2520.72354
## 128 129 130 131 132 133
## 2655.73093 7053.11722 1012.83043 -8499.00309 1749.90508 4136.14043
## 134 135 136 137 138 139
## -3163.47533 -1419.14090 -853.33889 -3879.34127 1183.84852 -494.73502
## 140 141 142 143 144 145
## -2912.86277 1718.98930 -1880.35456 -7828.54034 2040.47036 -3478.85714
## 146 147 148 149 150 151
## 2103.12804 -256.77048 1023.64397 -358.81274 1352.62579 1186.95030
## 152 153 154 155 156 157
## 3356.80487 -4861.71690 -1174.20441 -3235.50587 5957.13626 9746.79495
## 158 159 160 161 162 163
## -3645.96331 -4992.11213 3390.61622 -18.77984 2482.50382 -6124.73983
## 164 165 166 167 168 169
## -6961.26870 3943.41993 17178.08019 3403.16759 -625.06956 -2672.39259
## 170 171 172 173 174 175
## -1329.54197 3365.41883 -455.13047 -8302.35668 2640.03717 4099.95223
## 176 177 178 179 180 181
## 397.60729 8521.97684 -9481.32981 -3699.99443 -10971.51106 -11462.50043
## 182 183 184 185 186 187
## 1016.74962 9073.12876 -1656.15117 5702.28622 6324.13005 12920.96545
## 188 189 190 191 192 193
## 8182.10691 -4320.22573 2208.44381 10108.52244 -1913.23704 -2713.50383
## 194 195 196 197 198 199
## -10547.81417 -6622.22353 979.89205 -5487.69624 -10045.62529 5142.53318
## 200 201 202 203 204 205
## -3313.31684 -1954.60240 -1044.88736 6253.90940 9633.53253 317.60231
## 206 207 208 209 210 211
## 2662.53838 2832.63538 5516.19419 12560.43166 -5971.98093 -11572.66893
## 212 213 214 215 216 217
## -5929.55223 -10843.43505 -5319.60343 1287.88705 -13250.86565 16161.98943
## 218 219 220 221 222 223
## 7563.59441 1273.00725 26430.97434 12238.33425 7034.19303 13720.35667
## 224 225 226 227 228 229
## -4232.01359 -2050.31380 3474.50964 57.84660 2449.03501 8710.36650
## 230 231 232 233 234 235
## 5533.62947 -2200.91363 -2114.54480 9145.82212 -11794.10681 -7553.86087
## 236 237 238 239 240 241
## -8802.23544 -10352.86703 2836.67553 1107.78511 -8542.74532 -9226.59661
## 242 243 244 245 246 247
## 8866.32071 -8004.97126 2255.16470 -10537.44139 -4280.40648 1198.40846
## 248 249 250 251 252 253
## 774.59022 -12547.86922 3422.86555 1834.58265 3978.58372 1893.95536
## 254 255 256 257 258 259
## -1406.03732 10893.25226 20618.62771 2904.42896 -4567.99553 3819.64556
## 260 261 262 263 264 265
## -1988.77694 3447.00152 -5145.28987 -11178.66153 -4997.61920 -784.06222
## 266 267 268 269 270 271
## -5450.24806 8522.48587 -4550.59323 3924.30834 -2379.67787 4160.31779
## 272 273 274 275 276 277
## 430.58319 7022.76251 -1704.62913 11734.70732 -4895.60938 1421.86615
## 278 279 280 281 282 283
## -678.08671 7547.34309 -5374.12791 -3036.14289 -11558.67612 -2943.10385
## 284 285 286 287 288 289
## 18386.78220 7479.74902 2416.69153 -949.03188 589.44507 6082.35657
## 290 291 292 293 294 295
## 6556.31940 -19108.82054 -11430.01753 -8384.76787 9421.16161 2807.63570
## 296 297 298 299 300 301
## -1449.46444 27135.00026 9737.15567 4554.22185 9166.35962 2490.34984
## 302 303 304 305 306 307
## -1396.67048 7543.16779 -24658.72244 -3829.84893 -456.39187 -7244.63910
## 308 309 310 311 312 313
## -4227.32476 2688.94684 -9441.22615 -3453.98648 -8401.25712 1371.17217
## 314 315 316 317 318 319
## -3351.68844 1853.22300 -4286.71808 27247.90543 -1017.71763 3000.35823
## 320 321 322 323 324 325
## 10531.98138 5264.22350 32045.86219 4699.10193 -21346.05527 1455.16362
## 326 327 328 329 330 331
## 777.26156 -6792.69054 -2036.17794 -33558.64734 723.67911 -2461.89972
## 332 333 334 335 336 337
## -245.85100 -3321.08763 3940.00017 -597.54578 -7113.77627 -3259.13125
## 338 339 340 341 342 343
## -2328.30083 -7813.57883 3736.23254 -1505.23707 -1872.99780 -1128.95771
## 344 345 346 347 348 349
## 39.38541 338.59944 -1768.26358 -9596.41522 -13336.22528 2217.26849
## 350 351 352 353 354 355
## -4432.98185 -3762.90708 -6081.39862 1659.07482 1276.38563 2630.59200
## 356 357 358 359 360 361
## -3907.61558 -653.30939 533.99142 6860.54923 93.16844 -226.74229
## 362 363 364 365 366 367
## 2391.24015 -2953.59171 -1072.04516 -8935.83732 -4787.61706 -6359.89810
## 368 369 370 371 372 373
## -5078.46376 -7369.27461 4917.09054 244.78134 6984.07232 -7805.06720
## 374 375 376 377 378 379
## -2407.56533 -3528.97900 -2600.65611 -12587.56310 1811.50783 -10742.27087
## 380 381 382 383 384 385
## 5617.50771 9224.29421 2969.35566 -2573.84974 1433.35433 6561.57414
## 386 387 388 389 390 391
## 11197.91659 -6062.14901 -5603.74748 -380.67462 8338.57177 1555.65239
## 392 393 394 395 396 397
## 10955.66976 -10187.35691 2504.37097 433.38306 282.56598 -933.26319
## 398 399 400 401 402 403
## -837.36448 -14756.79697 8317.66708 -1415.20208 -1598.70981 6763.41555
## 404 405 406 407 408 409
## -8177.21534 -1505.70323 -2732.19064 -6006.86214 -3021.18167 -4067.91508
## 410 411 412 413 414 415
## -8891.97175 6028.49648 1502.96519 -7523.39356 -7816.71010 14121.47286
## 416 417 418 419 420 421
## 3646.05179 4298.05720 -8253.73576 -4929.24980 -2766.97812 2663.22556
## 422 423 424 425 426 427
## -14182.07681 -2907.10137 -9208.64003 2933.74350 6875.21226 6434.66081
## 428 429 430 431 432 433
## -4162.78156 -4282.79696 -4870.44485 -1922.53357 -5842.82511 -6740.09800
## 434 435 436 437 438 439
## -6043.53653 -1472.30384 -931.81518 -5066.14169 2500.66924 4736.76500
## 440 441 442 443 444 445
## -5188.84036 -2276.60533 1459.21800 -3968.28421 2713.54765 -6718.68302
## 446 447 448 449 450 451
## -12230.02130 -4589.92826 9574.31157 -2151.28298 4636.72211 -6012.22749
## 452 453 454 455 456 457
## -1246.49464 258.86318 2894.33948 -12417.07337 3263.76548 -6826.71473
## 458 459 460 461 462 463
## 6416.69931 2874.60359 2353.28437 -4012.23092 1939.13445 -171.78859
## 464 465 466 467 468 469
## 1626.60440 -696.21532 3176.36661 -2827.04034 5628.22835 -7140.80480
## 470 471 472 473 474 475
## -3133.26745 -2361.89077 -4811.94942 2864.93678 7651.43152 -6194.64404
## 476 477 478 479 480 481
## 1330.30230 -6339.27210 -2981.91596 1882.82136 -13069.73294 -9850.77377
## 482 483 484 485 486 487
## -1269.96036 -52.83174 -1044.78153 -1429.58594 -9675.80158 11030.31363
## 488 489 490 491 492 493
## 6120.04725 7276.68943 -5610.38866 5212.93602 9117.11281 5843.92447
## 494 495 496 497 498 499
## -13702.59657 -10740.30091 -3575.70508 -1229.95639 -647.56232 -7750.58170
## 500 501 502 503 504 505
## 510.99769 4180.33055 5381.16598 509.89972 -73.46016 -7394.06314
## 506 507 508 509 510 511
## 440.31196 -5182.58477 1713.99447 -1425.18073 -8284.89313 -703.51748
## 512 513 514 515 516 517
## -2779.29913 -688.43169 1227.82423 -9609.73660 -7851.40034 24219.66167
## 518 519 520 521 522 523
## 9663.58977 5681.79862 -5552.04030 2604.70908 16817.45455 11213.75222
## 524 525 526 527 528 529
## -24442.18478 -5256.08402 -3908.56629 4411.28801 -534.40691 -11278.27765
## 530 531 532 533 534 535
## 4256.24041 13756.19970 -5181.26860 4189.39198 5355.67783 -2007.66230
## 536 537 538 539 540 541
## -4747.92088 -7261.18942 -2259.84722 8171.02618 -54.63377 -8322.10850
## 542 543 544 545 546 547
## 1666.06969 -756.77703 210.90319 -11188.03133 -11180.00146 1957.57076
## 548 549 550 551 552 553
## 6905.93354 -1445.95208 710.01008 -7853.90011 8456.24166 762.67298
## 554 555 556 557 558 559
## -12093.61219 9053.08252 8509.91235 -82.63221 4671.21976 -3773.31767
## 560 561 562 563 564 565
## 13924.07173 21264.17437 -6773.83064 -9953.52483 6545.89276 -21.99872
## 566 567 568 569 570 571
## 3212.68580 -7624.78987 -17520.73164 6519.01386 6267.95845 1714.81065
## 572 573 574 575 576 577
## 2907.63342 1572.10759 -2364.72845 14529.39692 -9886.44340 -6441.91316
## 578 579 580 581 582 583
## 8539.24015 2656.81240 -6753.40200 7328.62926 -4004.32370 -2962.24192
## 584 585 586 587 588 589
## 15528.77459 -14726.78810 8257.01453 -128.71967 -6407.37297 -922.89923
## 590 591 592 593 594 595
## 88.17682 -10816.09898 1672.89500 -7276.31456 2959.46871 8744.09657
## 596 597 598 599 600 601
## -7656.26861 5721.88167 2582.92248 6694.04855 -3374.91321 5978.79965
## 602 603 604 605 606 607
## -8488.40660 2097.89998 1105.98912 2971.68214 1319.89419 219.94322
## 608 609 610 611 612 613
## -5986.09601 7922.26879 -1362.43055 -2744.88506 -3611.83423 -8372.55346
## 614 615 616 617 618 619
## 11843.26785 4779.34103 -9486.07912 11489.20691 5874.50281 -5764.37887
## 620 621 622 623 624 625
## 26192.96446 -13072.56873 -6971.47606 2994.55635 -4328.31605 -10743.69119
## 626 627 628 629 630 631
## 11181.36843 -21787.66176 -2497.64110 8595.45876 11024.05675 -1700.43301
## 632 633 634 635 636 637
## 33143.02048 -6829.56426 5506.67207 5179.50911 -2495.31378 -5554.52260
## 638 639 640 641 642 643
## -2124.31091 -12603.25055 -2372.19043 -2008.84222 -2637.49465 -2968.40565
## 644 645 646 647 648 649
## 1711.73216 4319.75582 16838.60982 18375.01644 653.19625 4565.31764
## 650 651 652 653 654 655
## 10382.55507 19899.80873 450.93593 -28338.89062 -1516.67210 -2454.11396
## 656 657 658 659 660 661
## 1719.60417 -3339.47943 -10754.19397 1565.48656 4123.12126 -1120.72672
## 662 663 664 665 666 667
## 12922.46701 1217.09053 1670.85648 -11834.18711 1270.24799 1076.16286
## 668 669 670 671 672 673
## -5278.46506 -7507.35945 1988.90338 -3795.65045 2597.71030 -3463.01053
## 674 675 676 677 678 679
## -9413.74908 -8364.29063 -3023.50092 125.85290 2792.86533 646.97881
## 680 681 682 683 684 685
## -3898.42627 -1875.32962 -1385.20897 -8310.54083 4594.23993 -2312.37973
## 686 687 688 689 690 691
## -1467.10153 517.83210 10779.16403 9749.43758 10503.70090 -9800.11410
## 692 693 694 695 696 697
## -3663.72080 -3239.13630 5779.46823 -10488.17852 -7990.94301 -8675.78552
## 698 699 700 701 702 703
## -6325.36651 -4783.47902 3040.57128 -4455.44598 -1948.50485 4170.21099
## 704 705 706 707 708 709
## 31042.61772 9424.33258 23350.46790 1584.62124 8237.33420 22840.85648
## 710 711 712 713 714 715
## 6484.06709 -18270.51019 4771.96004 -5490.59601 -142.93455 438.92731
## 716 717 718 719 720 721
## -17306.58464 -5298.74477 3301.69016 -3047.70227 -13011.77004 4249.97111
## 722 723 724 725 726 727
## -5587.91110 711.88100 -3966.16949 -12478.17332 1339.64915 -1896.50845
## 728 729 730 731 732 733
## -9807.35567 17241.07466 1741.08052 -2757.75236 5680.88345 -8666.74174
## 734 735 736 737 738 739
## -756.92754 8104.72324 -15387.77149 -5942.67894 7377.58605 -4818.12826
## 740 741 742 743 744 745
## 128.20807 1794.49168 -1990.09484 -5202.02400 6379.83974 -6310.15343
## 746 747 748 749 750 751
## 22665.24432 7787.36630 -1987.88118 -7327.60787 23379.74062 -4332.17510
## 752 753 754 755 756 757
## 1358.22346 -14459.28499 56075.90760 26883.32944 15061.25878 -10675.60395
## 758 759 760 761 762 763
## 10582.77913 7292.45422 5789.34647 -46407.54172 -16184.90812 953.26920
## 764 765 766 767 768 769
## -2530.87085 -3471.21751 122830.57427 19310.52072 43722.97357 22594.78887
## 770 771 772 773 774 775
## 12088.12017 15859.64901 25740.45065 -98793.97028 -6758.52226 -35834.83841
## 776 777 778 779 780 781
## 1728.27494 -1237.39274 3387.38318 -7422.82322 -1453.98482 -1954.20221
## 782 783 784 785 786 787
## 3415.49926 -7163.19632 -2245.47932 3910.83273 2257.91793 -2765.56039
## 788 789 790 791 792 793
## -4053.77406 1732.43372 2847.78936 -56.03670 -6707.56850 -5787.37672
## 794 795 796 797 798 799
## -1152.82583 -1268.49450 -7822.55637 -2360.24645 -3274.58419 -2672.19414
## 800 801 802 803 804 805
## 10717.58907 2236.64019 7075.76893 2920.97155 -5450.04682 8185.11876
## 806 807 808 809 810 811
## 9873.83789 -10601.30782 -7396.81982 -7506.27839 3004.06551 4193.13127
## 812 813 814 815 816 817
## -2269.91803 -14165.36804 -4112.52501 6257.34571 8235.95687 -9673.01196
## 818 819 820 821 822 823
## -7750.68400 -9337.43693 9751.94480 -1223.65839 -4372.45681 -8447.90700
## 824 825 826 827 828 829
## 7873.04061 7913.38800 4975.69119 -3101.76738 -711.60715 2892.18033
## 830 831 832 833 834 835
## 4668.83771 1624.80748 -6693.62674 2088.55979 -398.57224 2752.90482
## 836 837 838 839 840 841
## 4140.14005 -1136.94355 -9335.47495 5675.54646 -4861.73941 -7578.05470
## 842
## 3021.98760
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17250.64 20098.73 24354.08 24071.95 26426.07 23757.87 24474.42 19705.01
## 10 11 12 13 14 15 16 17
## 19441.48 16783.39 17561.11 14289.45 14340.64 15005.50 16702.62 15022.11
## 18 19 20 21 22 23 24 25
## 16057.10 15430.44 22515.15 21598.86 21078.56 22969.17 22294.94 22947.70
## 26 27 28 29 30 31 32 33
## 24794.24 18720.69 20447.39 28287.22 28344.90 28017.71 25646.36 27049.24
## 34 35 36 37 38 39 40 41
## 30893.51 31242.05 32651.67 30161.48 34138.56 37347.67 34402.83 31212.70
## 42 43 44 45 46 47 48 49
## 30060.10 20636.63 28157.76 30595.02 31684.57 38524.85 38019.27 42682.95
## 50 51 52 53 54 55 56 57
## 46920.57 39616.15 34182.87 29203.99 22346.23 28638.24 25217.79 21514.21
## 58 59 60 61 62 63 64 65
## 25923.98 27183.93 27481.03 27892.98 23762.60 40359.16 42203.71 37448.16
## 66 67 68 69 70 71 72 73
## 41656.44 46573.65 57245.22 55258.36 40496.86 38001.92 41020.78 35319.11
## 74 75 76 77 78 79 80 81
## 30769.91 21470.40 24672.89 20597.31 22684.03 17577.95 19594.02 18820.29
## 82 83 84 85 86 87 88 89
## 17847.71 15916.03 17194.21 20803.41 25222.53 26212.72 26237.68 26854.61
## 90 91 92 93 94 95 96 97
## 30981.00 29811.18 30810.86 28874.68 28074.24 28442.50 28849.21 22424.63
## 98 99 100 101 102 103 104 105
## 25440.62 18468.89 17324.66 15364.76 15664.61 16342.21 20816.27 19899.48
## 106 107 108 109 110 111 112 113
## 23411.69 23201.60 24878.24 27755.92 25253.24 21695.60 21971.11 24618.21
## 114 115 116 117 118 119 120 121
## 35486.14 33678.42 35516.48 38515.74 40472.26 38159.96 32979.35 29319.59
## 122 123 124 125 126 127 128 129
## 31403.07 29682.48 30864.41 38466.55 38108.90 37170.15 34032.70 35814.45
## 130 131 132 133 134 135 136 137
## 41214.03 40654.15 31853.09 33118.29 36309.05 32718.57 31105.34 30190.06
## 138 139 140 141 142 143 144 145
## 26746.01 28160.88 27930.43 25616.01 27641.07 26265.40 19865.53 22897.00
## 146 147 148 149 150 151 152 153
## 20723.01 23701.06 24241.21 25832.10 26014.23 27668.91 28970.05 32003.15
## 154 155 156 157 158 159 160 161
## 27471.92 26734.65 24289.15 30185.06 41666.39 39996.11 37360.24 42382.07
## 162 163 164 165 166 167 168 169
## 43791.07 47208.03 42672.55 37978.29 43405.21 59712.40 61925.21 60338.82
## 170 171 172 173 174 175 176 177
## 57163.54 55562.30 58265.70 57289.50 49579.25 52403.62 56147.39 56183.59
## 178 179 180 181 182 183 184 185
## 63314.62 53813.99 50563.94 41369.79 32906.54 36415.87 46522.44 45978.29
## 186 187 188 189 190 191 192 193
## 51932.87 57679.61 68465.89 73750.37 67443.13 67636.62 74709.09 70384.22
## 194 195 196 197 198 199 200 201
## 65905.67 55146.22 49174.54 50599.27 46192.63 38359.04 44785.75 43012.60
## 202 203 204 205 206 207 208 209
## 42650.46 43128.95 49925.04 58816.97 58446.46 60171.79 61828.09 65620.43
## 210 211 212 213 214 215 216 217
## 75089.84 67170.24 55355.70 49962.86 40956.46 37913.26 41027.87 31045.01
## 218 219 220 221 222 223 224 225
## 48023.69 55346.71 56248.88 79021.24 86518.52 88522.36 96116.01 87064.17
## 226 227 228 229 230 231 232 233
## 81060.78 80642.58 77291.54 76452.78 81191.23 82555.91 76989.69 72201.18
## 234 235 236 237 238 239 240 241
## 77856.54 64500.29 56534.38 48482.58 40091.61 44284.79 46438.17 39886.88
## 242 243 244 245 246 247 248 249
## 33564.54 43850.11 38095.26 42032.16 34293.69 32999.16 36655.55 39480.30
## 250 251 252 253 254 255 256 257
## 30306.99 36246.85 40049.42 45245.76 47964.89 47457.32 57761.37 75263.86
## 258 259 260 261 262 263 264 265
## 75078.85 68387.50 69869.78 66089.43 67536.00 61291.80 50563.19 46589.35
## 266 267 268 269 270 271 272 273
## 46798.82 42904.37 51711.16 47983.12 52131.11 50247.11 54315.70 54611.81
## 274 275 276 277 278 279 280 281
## 60631.06 58264.58 67940.47 61863.42 62073.52 60422.09 66166.70 59895.29
## 282 283 284 285 286 287 288 289
## 56458.10 46007.25 44403.50 61640.97 67172.74 67582.32 64999.13 64086.21
## 290 291 292 293 294 295 296 297
## 68088.39 71999.82 52990.59 43089.63 37098.84 47423.36 50666.18 49779.86
## 298 299 300 301 302 303 304 305
## 73983.56 79930.78 80598.64 85212.51 83410.53 78439.26 81907.15 56798.28
## 306 307 308 309 310 311 312 313
## 53058.25 52737.92 46526.18 43734.77 47339.23 39889.13 38610.83 33170.68
## 314 315 316 317 318 319 320 321
## 36956.40 36137.49 39970.15 37953.95 63748.29 61588.78 63212.88 71213.49
## 322 323 324 325 326 327 328 329
## 73601.57 99091.18 97468.34 73290.98 72088.45 70445.26 62394.46 59515.79
## 330 331 332 333 334 335 336 337
## 29454.75 33143.47 33583.14 35903.80 35244.43 41013.26 42089.20 37335.27
## 338 339 340 341 342 343 344 345
## 36549.44 36676.15 31993.62 37994.52 38658.14 38916.67 39792.76 41579.26
## 346 347 348 349 350 351 352 353
## 43401.84 43153.42 36095.80 26660.59 32006.98 30867.62 30457.54 28073.21
## 354 355 356 357 358 359 360 361
## 32753.61 36509.12 40974.19 39162.60 40423.29 42562.45 49960.12 50510.89
## 362 363 364 365 366 367 368 369
## 50712.62 53176.59 50659.19 50103.55 42746.33 39942.18 36117.89 33895.85
## 370 371 372 373 374 375 376 377
## 29952.34 37242.65 39530.36 47418.50 41388.14 40835.12 39371.94 38904.56
## 378 379 380 381 382 383 384 385
## 29769.21 34368.84 27418.21 35640.28 45976.79 49543.42 47816.22 49808.57
## 386 387 388 389 390 391 392 393
## 56030.80 65519.43 58728.46 53194.82 52923.43 60305.49 60829.04 69500.64
## 394 395 396 397 398 399 400 401
## 58602.63 60170.05 59730.01 59213.69 57700.08 56461.23 43215.33 51803.92
## 402 403 404 405 406 407 408 409
## 50804.00 49769.87 56173.36 48713.27 48024.19 46350.29 42026.04 40856.34
## 410 411 412 413 414 415 416 417
## 38919.54 33011.65 40887.18 43814.54 38485.00 33571.53 48448.38 52294.51
## 418 419 420 421 422 423 424 425
## 56225.16 48691.68 45013.69 43689.20 47276.93 35691.96 35421.07 29677.83
## 426 427 428 429 430 431 432 433
## 35269.64 43600.20 50494.78 47259.08 44326.73 41250.82 41138.97 37615.53
## 434 435 436 437 438 439 440 441
## 33752.54 30985.59 32562.24 34412.28 32416.19 37284.09 43491.84 40243.03
## 442 443 444 445 446 447 448 449
## 39948.92 42956.43 40841.74 44832.68 40077.88 31106.93 29943.97 41305.00
## 450 451 452 453 454 455 456 457
## 40986.42 46639.66 42274.21 42623.99 44245.09 47964.64 37835.23 42686.29
## 458 459 460 461 462 463 464 465
## 38107.87 45679.68 49201.00 51822.52 48550.87 50892.50 51094.11 52841.79
## 466 467 468 469 470 471 472 473
## 52339.20 55284.04 52611.34 57664.38 50921.84 48531.89 47117.52 43740.63
## 474 475 476 477 478 479 480 481
## 47498.14 54964.22 49389.13 51092.99 45879.92 44258.32 47092.30 36502.63
## 482 483 484 485 486 487 488 489
## 30061.82 31931.83 34629.50 36120.01 37086.23 30724.69 43259.52 49922.17
## 490 491 492 493 494 495 496 497
## 56754.96 51464.49 56299.32 63935.79 67748.60 53999.87 44574.28 42598.53
## 498 499 500 501 502 503 504 505
## 42921.85 43713.30 38198.00 40597.81 45901.26 51584.96 52294.89 52405.49
## 506 507 508 509 510 511 512 513
## 46105.12 47445.58 43703.43 46459.90 46125.46 39838.95 40970.44 40145.29
## 514 515 516 517 518 519 520 521
## 41251.32 43892.31 36729.83 32007.48 55905.84 64069.49 67723.75 61100.43
## 522 523 524 525 526 527 528 529
## 62440.40 76030.96 83010.18 57951.37 52819.57 49512.71 53893.26 53399.42
## 530 531 532 533 534 535 536 537
## 43579.47 48573.09 61238.13 55757.04 59155.89 63145.09 60196.64 55225.62
## 538 539 540 541 542 543 544 545
## 48685.56 47340.97 55280.92 55031.25 47588.64 49813.06 49639.67 50333.75
## 546 547 548 549 550 551 552 553
## 40979.43 32812.29 37155.64 45275.09 45071.99 46778.47 40786.19 49802.33
## 554 555 556 557 558 559 560 561
## 50958.04 40733.63 50277.94 58143.49 57508.21 61107.17 56872.93 68637.54
## 562 563 564 565 566 567 568 569
## 85331.97 75419.52 63979.11 68399.86 66523.60 67710.65 59277.73 43261.27
## 570 571 572 573 574 575 576 577
## 50272.33 56179.48 57362.65 59438.89 60086.16 57211.60 69462.44 58832.20
## 578 579 580 581 582 583 584 585
## 52553.05 60157.19 61661.69 54753.37 61022.04 56596.67 53640.23 67214.93
## 586 587 588 589 590 591 592 593
## 52638.56 59985.29 59077.37 52797.47 52102.39 52378.53 43091.25 45889.03
## 594 595 596 597 598 599 600 601
## 40513.67 44760.90 53527.13 46856.12 52717.08 55095.67 60766.63 56923.49
## 602 603 604 605 606 607 608 609
## 61738.84 53304.67 55185.30 55961.89 58270.82 58845.06 58385.67 52561.16
## 610 611 612 613 614 615 616 617
## 59625.14 57684.60 54780.83 51485.84 44446.45 55960.52 59849.22 50781.65
## 618 619 620 621 622 623 624 625
## 61187.07 65373.38 58861.04 81095.85 66213.76 58540.59 60544.17 55895.98
## 626 627 628 629 630 631 632 633
## 46228.20 56939.09 37489.07 37349.26 46920.66 57406.72 55450.69 84188.99
## 634 635 636 637 638 639 640 641
## 74372.04 76573.49 78211.31 72935.95 65652.88 62286.11 50187.19 48554.99
## 642 643 644 645 646 647 648 649
## 47446.21 45927.98 44312.12 46989.82 51608.68 66584.27 81013.09 78135.54
## 650 651 652 653 654 655 656 657
## 79039.59 84912.91 98361.78 93118.75 63379.53 60830.54 57783.97 58768.91
## 658 659 660 661 662 663 664 665
## 55208.77 45618.51 48003.59 52322.73 51514.68 63080.05 62957.71 63247.33
## 666 667 668 669 670 671 672 673
## 51699.18 53059.12 54077.89 49415.22 43393.10 46428.94 44027.00 47514.87
## 674 675 676 677 678 679 680 681
## 45266.61 38102.00 32758.36 32755.86 35505.71 40239.16 42500.28 40504.19
## 682 683 684 685 686 687 688 689
## 40527.78 40976.68 35317.33 41648.67 41145.96 41445.31 43441.41 54152.42
## 690 691 692 693 694 695 696 697
## 62612.30 70663.97 59957.58 55964.14 52845.53 58001.18 48291.09 41988.21
## 698 699 700 701 702 703 704 705
## 35882.08 32600.19 31079.71 36588.02 34851.08 35523.93 41458.67 70126.81
## 706 707 708 709 710 711 712 713
## 76287.25 93839.66 90157.81 92753.86 107783.50 106623.80 83978.90 84326.31
## 714 715 716 717 718 719 720 721
## 75662.08 72763.93 70739.87 53464.46 48861.45 52354.56 49858.63 38970.60
## 722 723 724 725 726 727 728 729
## 44540.20 40810.40 43056.17 40930.74 31635.35 35587.22 36212.64 29846.35
## 730 731 732 733 734 735 736 737
## 47919.21 50167.47 48200.83 53856.31 46260.78 46535.42 54519.06 40966.82
## 738 739 740 741 742 743 744 745
## 37377.84 45881.41 42655.08 44158.08 46927.52 46040.45 42458.59 49449.30
## 746 747 748 749 750 751 752 753
## 44469.04 65436.92 70758.60 66866.89 58800.12 78584.32 71656.78 70575.71
## 754 755 756 757 758 759 760 761
## 55809.09 104541.81 121616.74 126206.89 107728.08 110156.97 109404.22 107432.97
## 762 763 764 765 766 767 768 769
## 60098.77 45146.02 47055.73 45679.93 43656.00 152254.76 156692.74 181903.35
## 770 771 772 773 774 775 776 777
## 185470.74 179406.92 177403.84 184287.68 81480.09 72066.98 38433.44 41867.25
## 778 779 780 781 782 783 784 785
## 42276.33 46675.11 41072.56 41392.63 41235.22 45789.91 40525.91 40223.31
## 786 787 788 789 790 791 792 793
## 45338.51 48363.99 46618.06 43966.71 46706.07 50074.47 50480.43 45022.81
## 794 795 796 797 798 799 800 801
## 41057.83 41642.92 42053.13 36684.39 36766.16 36038.62 35929.27 47534.22
## 802 803 804 805 806 807 808 809
## 50264.09 56878.17 59027.19 53590.17 60754.02 68489.74 57357.53 50429.99
## 810 811 812 813 814 815 816 817
## 44280.79 48091.73 52460.92 50631.23 38637.67 36941.80 44521.47 52873.87
## 818 819 820 821 822 823 824 825
## 44522.97 38905.44 32610.06 43789.94 43968.46 41372.91 35543.53 44711.47
## 826 827 828 829 830 831 832 833
## 52758.02 57222.34 54065.04 53394.68 55958.02 59750.48 60404.48 53707.01
## 834 835 836 837 838 839 840 841
## 55528.72 54947.24 57193.00 60367.66 58530.47 49760.88 55214.88 50772.91
## 842
## 44517.73
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8101
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.1412 0.7756474 3.957983
## t2* 2701.5295 168.5381355 898.983402
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.181985 5.074714 13.5417
## 2 lag_depvar 1637.793928 2735.785587 4553.8984Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jun 9 01:00:57 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jun 9 01:01:07 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jun 9 01:01:17 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jun 9 01:01:26 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jun 9 01:01:36 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jun 9 01:01:46 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jun 9 01:01:56 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jun 9 01:02:05 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jun 9 01:02:15 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jun 9 01:02:25 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 11.4882 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 250.2842 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 55.4514 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 2.6380 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 33.1500 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 20.8978 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 17.5960 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 391.5056 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2716, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2716 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-06-09 00:04:58 sería de: 26.669 pesos// Percentil 95% más alto proyectado: 35.110,81
Según TimeGPT: La proyección de la UF a 298 días más 2026-04-03 sería de: 40.528,13 pesos// Percentil 80% más alto proyectado: 40.841,36 pesos// Percentil 95% más alto proyectado: 41.156,85
Según prophet: La proyección de la UF a 298 días más 2026-04-03 sería de: 41.051 pesos// Percentil 95% más alto proyectado: 46.675
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26284.61 | 26323.83 |
| Lo.80 | 26417.00 | 26488.31 |
| Point.Forecast | 26668.91 | 26799.01 |
| Hi.80 | 31482.21 | 32162.56 |
| Hi.95 | 34372.51 | 35001.85 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5517 -0.2958
## s.e. 0.1178 0.1321
##
## sigma^2 = 37898: log likelihood = -494.63
## AIC=995.27 AICc=995.61 BIC=1002.18paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3686 591.0263 14.1117
## s.e. 0.0989 262.2242 8.0260
##
## sigma^2 = 36262: log likelihood = -498.66
## AIC=1005.31 AICc=1005.89 BIC=1014.58forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 678.4072 | 646.2154 | 701.4202 |
| Lo.80 | 816.0921 | 790.9497 | 796.5527 |
| Point.Forecast | 1076.1852 | 1064.3594 | 1012.6735 |
| Hi.80 | 1336.2783 | 1363.0738 | 1287.0884 |
| Hi.95 | 1473.9633 | 1521.2037 | 1461.1317 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "C:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 autoplotly_0.1.4
## [13] rvest_1.0.4 plotly_4.10.4 xts_0.14.1
## [16] forecast_8.24.0 wordcloud_2.6 RColorBrewer_1.1-3
## [19] SnowballC_0.7.1 tm_0.7-16 NLP_0.3-2
## [22] tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.0
## [25] dplyr_1.1.4 purrr_1.0.4 tidyr_1.3.1
## [28] tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0
## [31] sjPlot_2.8.17 lattice_0.22-6 gridExtra_2.3
## [34] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [37] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [40] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.4
## [43] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [46] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.1.0
## [4] janitor_2.2.1 lifecycle_1.0.4 httr2_1.1.2
## [7] StanHeaders_2.32.10 globals_0.18.0 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.3.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] askpass_1.2.1 pkgbuild_1.4.8 DBI_1.2.3
## [22] abind_1.4-8 quadprog_1.5-8 nnet_7.3-19
## [25] rappdirs_0.3.3 inline_0.3.21 data.tree_1.1.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.11
## [31] performance_0.14.0 spatial_7.3-17 parallelly_1.45.0
## [34] codetools_0.2-20 xml2_1.3.8 tidyselect_1.2.1
## [37] ggeffects_2.2.1 farver_2.1.2 urca_1.3-4
## [40] its.analysis_1.6.0 matrixStats_1.5.0 stats4_4.4.0
## [43] jsonlite_2.0.0 ellipsis_0.3.2 Formula_1.2-5
## [46] systemfonts_1.2.3 tools_4.4.0 glue_1.8.0
## [49] xfun_0.52 TTR_0.24.4 ggfortify_0.4.17
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.3
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 colorspace_2.1-1 networkD3_0.4.1
## [64] gtools_3.9.5 generics_0.1.4 htmlwidgets_1.6.4
## [67] pkgconfig_2.0.3 gtable_0.3.6 timeDate_4041.110
## [70] lmtest_0.9-40 selectr_0.4-2 janeaustenr_1.0.0
## [73] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [76] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [79] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [82] curl_6.3.0 cachem_1.1.0 sjlabelled_1.2.0
## [85] KernSmooth_2.23-22 parallel_4.4.0 fBasics_4041.97
## [88] pillar_1.10.2 vctrs_0.6.5 gplots_3.2.0
## [91] slam_0.1-55 car_3.1-3 dbplyr_2.5.0
## [94] evaluate_1.0.3 cli_3.6.5 compiler_4.4.0
## [97] crayon_1.5.3 future.apply_1.20.0 labeling_0.4.3
## [100] sjmisc_2.8.10 rstan_2.32.7 QuickJSR_1.7.0
## [103] viridisLite_0.4.2 assertthat_0.2.1 lazyeval_0.2.2
## [106] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [109] bit64_4.6.0-1 future_1.58.0 nixtlar_0.6.2
## [112] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [115] bslib_0.9.0 quantmod_0.4.27 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))